Causality Reading Group (archive)

This page archives the meetings of the Causality Reading Group from 2014 to 2020. Currently, the reading group has been put on hold until further notice.

Schedule 2020

June 1114:00ZoomA Bayesian Nonparametric Conditional Two-sample Test with an Application to Local Causal Discovery by Boeken and MooijPhilip Boeken
June 414:00ZoomA Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms by Bengio et al.Noud
May 2714:00ZoomConstraint-Based Causal Discovery In The Presence Of Cycles by Joris Mooij and Tom ClaassenJoris Mooij
May 1414:00ZoomDesigning Data Augmentation for Simulating Interventions by Maximilian Ilse, Jakub Tomczak and Patrick ForréMaximilian Ilse
May 714:00ZoomCausal Discovery in the Presence of Missing Data by Tu et. al.Philip Versteeg
Apr 3014:00ZoomLearning stable and predictive structures in kinetic systemsStephan Bongers
Apr 2314:00ZoomA correspondence principle for simultaneous equation modelsTineke Blom
Apr 914:00ZoomCausally Correct Partial Models for Reinforcement LearningNoud de Kroon
Apr 214:00ZoomOut-of-Distribution Generalization via Risk ExtrapolationPatrick Forré
Mar 1914:00ZoomConstraint-based Causal Structure Learning with Consistent Separating SetsPhilip Versteeg
Mar 514:00F2.02Achieving Robustness in the Wild via Adversarial Mixing with Disentangled RepresentationsNoud de Kroon
Jan 1413:00C3.163Integrating Markov processes with structural causal modeling enables counterfactual inference in complex systems by Ness et al.Tineke Blom

Schedule 2019

Dec 1713:00C3.163A Bayesian nonparametric test for conditional independence by Onur TeymurPatrick Forré
Dec 313:00C3.163Causal Regularization by Dominik JanzingPhilip Versteeg
Nov 2613:00C3.163Adjacency-Faithfulness and Conservative Causal Inference by Joseph Ramsey et al.Alexander Marx
Oct 2213:00C3.163Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation by Ruibo Tu et al.Noud de Kroon
Sep 2413:00C3.163Approximate Causal Abstraction by Sander Beckers et al.Stephan Bongers
Sep 1013:00C3.163Active Causal Discovery by Predicting Counterfactual OutcomesAron Hammond
Aug 2713:00C3.163Invariant Risk Minimization by Martin Arjovsky et al.Patrick Forré
Aug 1313:00C3.163Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-based Approach by Tikka et al.Philip Versteeg
July 1613:00C3.163Density estimation using Real NVP by Laurent Dinh et al.Stephan Bongers
July 213:00C3.163Abstracting Causal Models by Sander Beckers and Joseph Y. HalpernTineke Blom
June 1813:00C3.163Causal Confusion in Imitation Learning by De Haan et al.Noud de Kroon
June 1113:00C3.163 Orthogonal Structure Search for Efficient Causal Discovery from Observational Data by Raj et al.Phillip Versteeg
May 1413:00C3.163Learning Disentangled Representations with Semi-Supervised Deep Generative Models by Siddharth et al.Stephan Bongers
Apr 3013:00C3.163Structural Causal Bandits: Where to Intervene? by Lee and BareinboimNoud de Kroon
Apr 1613:00C3.163Defining Network Topologies that Can Achieve Biochemical Adaptation by Ma et al. and Perfect and Near-Perfect Adaptation in Cell Signaling by FerrellTineke Blom
Mar 19 2613:00C3.163Dynamic Chain Graph Models for Ordinal Time Series Data by Behrouzi et al.Pariya Behrouzi
Feb 1213:00C3.163Causal Reasoning from Meta-reinforcement Learning by Dasgupta et al.Noud de Kroon
Feb 513:00A1.14Small workshop with presentations (mostly) on counterfactuals by Robert van Rooij, Katrin Schultz, and Joris MooijJoris Mooij
Jan 2913:00C2.109Cause-Effect Deep Information Bottleneck For Incomplete Covariates by Parbhoo et al. (2018)Stephan Bongers
Jan 1513:00C3.163Equality of Opportunity in Classification: A Causal Approach by Junzhe Zhang and Elias Bareinboim (2018)Tineke Blom

Schedule 2018

Dec 1813:00C2.109Learning Predictive Models That Transport by Subbaswamy et al. (2018)Thijs van Ommen
Dec 413:00C3.163Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search by Buesing et al. (2018)Noud de Kroon
Nov 2713:00C2.109Multi-domain Causal Structure Learning in Linear Systems by Ghassami et al. (2018)Philip Versteeg
Nov 2013:00C3.163Multiple Causal Inference with Latent Confounding by Ranganath and Perotte (2018)Stephan Bongers
Nov 1313:00C3.163A Constraint-Based Algorithm For Causal Discovery with Cycles, Latent Variables and Selection Bias by Strobl (2018)Patrick Forré
Oct 2314:00C3.163Model selection and local geometry by Evans (2018)Thijs van Ommen
Oct 1614:00C3.163Learning Functional Causal Models with Generative Neural Networks by Goudet et al. (2017)Philip Versteeg
Oct 914:00C3.163Causal Learning for Partially Observed Stochastic Dynamical Systems by Mogensen et al. (2018)Stephan Bongers
Oct 214:00C3.163The inflation technique solves completely the classical inference problem by Navascues and Wolf (2017)Patrick Forré
Sep 2514:00C2.109The Inflation Technique for Causal Inference with Latent Variables by Wolf et al. (2018) [part 2]Tineke Blom
Sep 1814:00C3.146The Inflation Technique for Causal Inference with Latent Variables by Wolf et al. (2018) [part 1]Tineke Blom
Sep 1114:00C3.163The Inferelator by Bonneau et al. (2016)Joris Mooij
Jul 1215:00C3.146Counterfactual Risk Minimization: Learning from Logged Bandit Feedback (2015) by Swaminathan and Joachims Philip Versteeg
Jun 2815:00C3.146Causality and model abstraction by Iwasaki and Simon (1994) [part 2]Stephan Bongers
Jun 1415:00C3.146The blessing of multiple causes by Wang and Blei (2018)Patrick Forré
Jun 715:00C3.146Paper DraftThijs van Ommen
May 2415:00C3.146The Blessings of Multiple Causes by Wang and Blei (2018Patrick Forré
May 1715:00C3.146Information Processing Features Can Detect Behavioral Regimes of Dynamical Systems by Quax et al. (2017)Rick Quax
Apr 2615:00C3.146Causality and model abstraction by Iwasaki and Simon (1994) [part 1]Tineke Blom
Apr 1215:00C3.146Joint Causal Inference from Multiple Datasets by et al. (2018)Joris Mooij
Apr 515:00C3.146Efficient Structure Learning of Bayesian Networks using Constraints by de Campos and Ji (2011)Thijs van Ommen
Mar 2915:00C3.146Consistency Guarantees for Permutation-Based Causal Inference Algorithms by Solus et al. (2017) Philip Versteeg
Mar 215:00C3.146On the latent space of Wasserstein Auto-Encoders by Rubinstein et al . (2018)Stephan Bongers
Feb 1515:00C3.146 Predictive Independence Testing, Predictive Conditional Independence Testing, and Predictive Graphical Modelling by Burkart and Király (2017)Patrick Forré
Jan 1115:00C3.146Extended Conditional Independence and Applications in Causal Inference by Constantinou and Dawid (2017)Patrick Forré

Schedule 2017

Dec 2115:00C3.146Influence of node abundance on signaling network state and dynamics analyzed by mass cytometry by Lun et al.(2017)Tineke Blom
Nov 1615:00C3.146Causal inference using the algorithmic Markov condition by Janzing and Schoelkopf (2008) and Causal Markov condition for submodular information measures by Steudel et al. (2010)Patrick Forré
Nov 915:00C3.146Telling Cause from Effect using MDL-based Local and Global Regression by Marx and Vreeken (2017)Thijs van Ommen
Nov 215:00C3.146Implicit Causal Models for Genome-wide Association Studies by Tran and Blei (2017)Joris Mooij
Oct 2815:00C3.146Structure Learning of Linear Gaussian Structural Equation Models with Weak Edges by Eigenmann et al. (2017)Tineke Blom
Oct 1215:00C3.146Identifying Best Interventions through Online Importance Sampling by Sen et al. (2017)Philip Versteeg
Sep 2815:00C3.146Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information by Jakob Runge (2017)Patrick Forré
Sep 2115:00C3.146Avoiding Discrimination through Causal Reasoning by Kilbertus et al.  (2017) Sara Magliacane
Sep 1415:00C3.146CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training by Kocaoglu et al. (2017)Patrick Forré
Aug 2514:00C3.146Paper draftPatrick Forré
Aug 1814:00C3.146Ch5-8 of Counterfactual Reasoning and Learning Systems by Bottou et al. (2013)Philip Versteeg
Aug 1114:00C3.146Ch1-4 of Counterfactual Reasoning and Learning Systems by Bottou et al. (2013)Philip Versteeg
Jul 2814:00C3.146Discovering Causal Signals in Images by Lopez-Paz et al. (2017)Patrick Forré
Jul 2114:00C3.146Margins of discrete Bayesian networks by Evans (preprint)Thijs van Ommen
Jul 1414:00C3.146Revisiting Classifier Two-Sample Tests by D. Lopez-Paz and M. Oquab (2016)Stephan Bongers
Jul 714:00C3.146Causal Discovery in the Presence of Measurement Error: Identifiability Conditions by Zhang (2017)Tineke Blom
Jun 1614:00C3.146Zhang et al. (2013), Zhang et al. (2015) and Gong et al. (2016)Tineke Blom, Stephan Bongers and Thijs van Ommen
May 1214:00C3.146On Causal and Anticausal Learning by Scholkopf et al (2012)Sara Magliacane
Mar 1714:00C3.146Paper draftStephan Bongers
Mar 1014:00C3.146Strong completeness and faithfulness in Bayesian networks by Meek (1995)Joris Mooij
Mar 314:00C3.146Unifying Markov Properties for Graphical Models by Lauritzen and Sadeghi (preprint)Patrick Forré
Feb 2414:00C3.146Causal Bandits: Learning Good Interventions via Causal Inference by Lattimore, Lattimore and Reid (2016)Stephan Bongers
Feb 1714:00C3.146Bandits with Unobserved Confounders: A Causal Approach by Bareinboim, Forney and Pearl (2016)Philip Versteeg
Feb 1014:00C3.146Joint Causal Inference (2016) by Magliacane, Claassen and MooijSara Magliacane
Feb 314:00C3.146Paper draftChristos Louizos
Jan 2714:00C3.146Identification of Joint Interventional Distributions in Recursive Semi-Markovian Causal Models (2006) by Shpitser and PearlPatrick Forré
Jan 1314:00C3.146Causal inference and the data-fusion problem by Bareinboim and Pearl (2016)Philip Versteeg

Schedule 2017

Jan 0814:30C3.146Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing by Benjamini and HochbergPhilip Versteeg
Feb 0514:00C3.146Causation Prediction and Search (chapters 1&2) by Spirtes and Glymour and ScheinesJoris Mooij
Feb 1214:00C3.146Causation Prediction and Search (chapter 3) by Spirtes and Glymour and ScheinesAlexander Ly
Feb 1914:00C3.146Causation Prediction and Search (3.5 – 3.9) by Spirtes and Glymour and ScheinesAlexander Ly
Feb 2614:00C3.146Causation Prediction and Search (chapter 4) by Spirtes and Glymour and ScheinesThijs van Ommen
Mar 414:00C3.146Causation Prediction and Search (chapter 5.1-5.4) by Spirtes and Glymour and ScheinesStephan Bongers
Mar 1114:00C3.146Causation Prediction and Search (chapter 5.5-5.10) by Spirtes and Glymour and ScheinesPhilip Versteeg
Mar 1814:00C3.146Causation Prediction and Search (chapter 6) by Spirtes and Glymour and ScheinesSara Magliacane
Apr 1514:00C3.146Causation Prediction and Search (chapter 7) by Spirtes and Glymour and ScheincesJoris Mooij
Apr 2214:00C3.146Inferring the Causal Direction Privately by Kusner and Sun and Sridharan and WeinbergerMijung Park
Apr 2914:00C3.146On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias by Zhang (2008)Joris Mooij
May 1314:00C3.146 Causal inference using invariant prediction: identification and confidence intervals by Peters and Bühlmann and Meinshausen (2016)Alexander Ly
May 2014:00C3.146 Quantifying Causal Influences (2012) by Janzing and Balduzzi and Grosse-Wentrup and SchölkopfRick Quax
May 2714:00C3.146Extending Factor Graphs so as to Unify Directed and Undirected Graphical Models by Frey (2003) and Causality with Gates (2012) by WinnSara Magliacane
Jul 114:00C3.146Stephan's draft on Markov properties of graphical representations of acyclic structural causal modelsStephan Bongers
Jul 814:00C3.146The central role of the propensity score in observational studies for causal effects (1983) by Rosenbaum and RubinThomas Klaus
Jul 2214:00C3.146Learning Optimal Interventions by Mueller and Reshef and Du and JaakkolaMijung Park
Jul 2914:00C3.146ICML 2016 Tutorial Causal Inference for Observational Studies by David Sontag and Uri ShalitJoris Mooij
Aug 1214:00C3.146Half-trek criterion for generic identifiability of linear structural equation models by Foygel and Draisma and DrtonThijs van Ommen
Aug 1914:00C3.146Graphs for Margins of Bayesian Networks (2016) by Robin EvansPatrick Forré
August 2614:00C3.146Estimating and Controlling the False Discovery Rate for the PC Algorithm Using Edge-Specific P-Values (2016) by Strobl and Spirtes and VisweswaranSara Magliacane
Sep 0914:00C3.146The Logic of Structure-Based Counterfactuals [sections 7.1-7.3 in Causality: Models Reasoning and Inference (2009)] by Judea PearlJoris Mooij
Sep 1614:00C3.146Some Title by Peters Janzing and Schölkopf (2016) [ch. 1]Stephan Bongers
Sep 2314:00C3.146Some Title by Peters Janzing and Schölkopf (2016) [chs. 2-3]Stephan Bongers
TBA14:00C3.146Batch Learning from Logged Bandit Feedback through Counterfactual Risk Minimization (2015) by Swaminathan and JoachimsThorsten Joachims
Nov 414:00C3.146Ancestral Graph Markov Models by Richardson and Spirtes (2002) [ch. 1-3]Joris Mooij
Nov 1114:00C3.146Ancestral Graph Markov Models by Richardson and Spirtes (2002) [ch. 4-6] Tineke Blom
Nov 1814:00C3.146Ancestral Graph Markov Models by Richardson and Spirtes (2002) [ch. 6–7]Tineke Blom
Nov 2514:00C3.146Ancestral Graph Markov Models by Richardson and Spirtes (2002) [ch. 8-10] Thijs van Ommen
Dec  1614:00C3.146Identifying independence in Bayesian Networks by Geiger Verma and Pearl (1990) Tom Claassen

Schedule 2015

2015/01/30Computing Maximum Likelihood Estimates in Recursive Linear Models with Correlated Errors by M. Drton, M. Eichler, T. S. RichardsonNicholas Cornia
2015/02/13Constraint-based Causal Discovery: Conflict Resolution with Answer Set Programming, and supplement, by A. Hyttinen, F. Eberhardt, and M. JärvisaloSara Magliacane
2015/02/20Enriching for direct regulatory targets in perturbed gene-expression profiles by S. G. Tringe, A. Wagner, S. W. RubyPhilip Versteeg
2015/02/27Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs by A. Hauser and P. BühlmannJoris Mooij
2015/03/06Causal Discovery from Changes by J. Tian and J. PearlSara Magliacane
2015/03/13Causal Discovery from Changes: a Bayesian Approach by J. Tian and J. PearlPhilip Versteeg
2015/03/20Constraint-based Causal Discovery from Multiple Interventions over Overlapping Variable Sets (pp. 1-12) by S. Triantafillou and I. TsamardinosNicholas Cornia
2015/03/27Constraint-based Causal Discovery from Multiple Interventions over Overlapping Variable Sets (pp. 13-22) by S. Triantafillou and I. TsamardinosPhilip Versteeg
2015/04/16Constraint-based Causal Discovery from Multiple Interventions over Overlapping Variable Sets (pp. 23-47) by S. Triantafillou and I. Tsamardinos; Statistical significance for genomewide studies by J.D. Storey and R. TibshiraniJoris Mooij
2015/06/12Feedback models interpretation and discovery, chapter 2 of PhD thesis of Thomas RichardsonMartin Gullaksen
2015/07/03Advances in Bayesian Network Learning using Integer Programming by Bartlett and CussensSara Magliacane
2015/07/24backShift: Learning causal cyclic graphs from unknown shift interventions by Rothenhäsler, Heinze, Peters, MeinshausenJoris Mooij
2015/07/31Computing Maximum Likelihood Estimates in Recursive Linear Models with Correlated Errors by M. Drton, M. Eichler, and T. S. RichardsonJoris Mooij
2015/08/28Single timepoint models of dynamic systems by K. Sachs, S. Itani, J. Fitzgerald, B. Schoeberl, G.P. Nolan, C.J. TomlinJoris Mooij
2015/09/11UAI tutorial Non-parametric Causal Models by Richardson and Evans (part 1a; slides, video)-
2015/09/18UAI tutorial Non-parametric Causal Models by Richardson and Evans (part 1b; slides, video)-
2015/09/30Studies in Causal Reasoning and Learning (ch. 1, 4.1, 4.2, 4.3) by Jin TianJoris Mooij
2015/10/09Influence Diagrams by Howard and Matheson & Influence Diagrams - Historical and Personal Perspectives by Pearl & Influence Diagrams for Causal Modelling and Inference by A.P. DawidDiederik Roijers
2015/10/16Distribution-Free Learning of Bayesian Network Structure in Continuous Domains by D. MargaritisSara Magliacane
2015/10/23Graphs for margins of Bayesian networks (without section 6) by R. EvansStephan Bongers
2015/11/06unspecifiedDiederik Roijers
2015/11/13Inferring latent structures via information inequalities by Chaves, Luft, Maciel, Gross, Janzing and SchölkopfPhilip Versteeg
2015/11/30 11:00-12:00Independence Properties of Directed Markov Fields by Lauritzen, Dawid, Larsen, LeimerTBA
2015/12/11Single World Intervention Graphs: A Primer by Richardson and RobinsJoris
2015/12/22Single World Intervention Graphs: A Primer by Richardson and RobinsTBA

Schedule 2014

2014/05/23Chain graph models and their causal interpretations by S.L. Lauritzen and T.S. RichardsonJoris Mooij
2014/06/13Classification using Discriminative Restricted Boltzmann Machines by H. Larochelle and Y. BengioSergio Mota

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